基于稀疏自动编码器的发动机机载模型建模方法研究

Research on Modeling Method of On-Board Engine Model Based on Sparse Auto-Encoder

关键词:  机载发动机自适应模型  智能发动机控制  BP算法  稀疏自动编码器  采样数据

Key words:  Adaptive on-board engine model  Intelligent engine control  BP algorithm  Sparse auto-encoder  Sampled data

摘要:

为解决分段线性化机载模型精度不足的问题，提出并设计了基于稀疏自动编码器的大包线、具有10输入11输出的发动机机载自适应模型，该模型由稳态、动态两部分组合而成。首先基于一种新的相似准则进行建模所需样本数据的压缩，在保留主要信息的同时，大大降低了数据量及采样时间。用BP算法对简化后的样本数据进行了机载模型稳态部分的建模。针对机载模型动态部分所需样本数据量巨大、BP算法难以训练的问题，建立了基于稀疏自动编码器的动态机载模型。引入准稳态判断逻辑，在动态过程使用稀疏自动编码器的动态机载模型，在稳态过程使用基于BP算法的稳态机载模型。仿真结果表明，所建立的发动机机载模型具有优良的动稳态精度，且实时性好、存储量小，其中动态精度小于1%，稳态精度小于0.6%，一次模型计算时间不大于1ms，模型存储量不大于100kB。

Abstract:

In order to solve the problem of the low accuracy of the piecewise linear model in the development of the on-board engine model，based on the sparse auto-encoder，an adaptive on-board engine model with 10 inputs 11 outputs for the large envelope is proposed and designed，the model consists of steady and dynamic two parts. In the first place，a new similarity criterion is needed to compress the sample data，which can reduce the amount of data and the sampling time while retaining the main information. Steady on-board engine modeling work is completed by the BP algorithm with the simplified training data. In view of the huge amount of data needed in dynamic modeling，the BP algorithm is difficult to train. Dynamic on-board model is established based on the sparse auto-encoder. By the introduction of quasi steady state judgment logic，in the dynamic process the dynamic on-board model based on the sparse auto-encoder is used，while in the steady state process the steady on-board model based on the BP algorithm is used. Simulation results show that the on-board model obtained has excellent dynamic and steady state accuracy，good real-time performance and a small amount of storage. The dynamic accuracy is within 1%，the steady accuracy is within 0.6%，model computation time is within 1ms once with the storage capacity no more than 100kB.